Problem: District heating systems (DHS) form the backbone of urban infrastructure, delivering thermal energy and hot water to buildings via underground pipelines. These systems consist of heat generators (boilers, thermal power plants, or other sources) that heat water or steam and circulate it to meet heating demands. However, optimizing their performance poses complex challenges, including reducing energy consumption, improving service quality, and minimizing environmental impact. It was requested to developed an innovative solution to address these challenges by predicting the return water temperature in DHS using AI and machine learning.
Objective: To create an AI-based model that accurately predicts return water temperature in district heating systems, optimizing energy use, improving heating quality, preventing failures, streamlining operations, and reducing environmental impact.
Proposal: AI GEEKS will create a machine learning model using historical and real-time DHS data to: first predict return water temperature with ±1°C accuracy for 24 hours and ±14°C for weekly forecasts. Second, optimize system operations by forecasting related parameters like pressure and weight.
Market Advantages: Accurate temperature forecasts ensure improved heating quality by maintaining consistent indoor temperatures despite fluctuating weather conditions, ensuring comfort. It also aids in accident prevention by detecting anomalies in temperature trends early, preventing system overloads, and avoiding equipment damage. Forecasting supports operational planning, optimizing equipment schedules and reducing system strain, particularly during peak demand periods. Cost reduction is another benefit, as temperature predictions lead to better fuel calculations and reduced waste, ultimately lowering fuel expenses. Finally, it helps mitigate environmental impacts by reducing CO2 emissions and other pollutants through optimized system operations, resulting in less fuel consumption and a cleaner environment.
Solution: The dataset used for model training consists of 32,547 hourly records, incorporating key input variables such as operating time, feed temperature, feed pressure, feed weight, return temperature, return pressure, return weight, temperature and weight differences, thermal energy, and mass imbalance. The target variable for the model is the prediction of return temperature (temp_return). AI GEEKS explored various machine learning models to develop a predictive solution that accurately forecasts return water temperature. The results showed a precision of ±1.17°C for a 24-hour forecast and ±14°C for a one-week forecast. The final model demonstrated high accuracy, with the ability to predict return water temperature with a precision of up to ±1°C. It also extended its predictions to related parameters such as return water pressure and return water weight, showcasing its broader predictive capabilities and potential future impact in optimizing system operations.
AI GEEKS' predictive model represents a transformative approach to DHS management, delivering measurable benefits in cost, efficiency, and sustainability. With this solution, urban heating systems can achieve optimal performance, benefiting operators, consumers, and the environment alike.
We welcome the opportunity to collaborate on this groundbreaking initiative.
Proposal: AI GEEKS will create a machine learning model using historical and real-time DHS data to: first predict return water temperature with ±1°C accuracy for 24 hours and ±14°C for weekly forecasts. Second, optimize system operations by forecasting related parameters like pressure and weight.
Market Advantages: Accurate temperature forecasts ensure improved heating quality by maintaining consistent indoor temperatures despite fluctuating weather conditions, ensuring comfort. It also aids in accident prevention by detecting anomalies in temperature trends early, preventing system overloads, and avoiding equipment damage. Forecasting supports operational planning, optimizing equipment schedules and reducing system strain, particularly during peak demand periods. Cost reduction is another benefit, as temperature predictions lead to better fuel calculations and reduced waste, ultimately lowering fuel expenses. Finally, it helps mitigate environmental impacts by reducing CO2 emissions and other pollutants through optimized system operations, resulting in less fuel consumption and a cleaner environment.
Solution: The dataset used for model training consists of 32,547 hourly records, incorporating key input variables such as operating time, feed temperature, feed pressure, feed weight, return temperature, return pressure, return weight, temperature and weight differences, thermal energy, and mass imbalance. The target variable for the model is the prediction of return temperature (temp_return). AI GEEKS explored various machine learning models to develop a predictive solution that accurately forecasts return water temperature. The results showed a precision of ±1.17°C for a 24-hour forecast and ±14°C for a one-week forecast. The final model demonstrated high accuracy, with the ability to predict return water temperature with a precision of up to ±1°C. It also extended its predictions to related parameters such as return water pressure and return water weight, showcasing its broader predictive capabilities and potential future impact in optimizing system operations.
AI GEEKS' predictive model represents a transformative approach to DHS management, delivering measurable benefits in cost, efficiency, and sustainability. With this solution, urban heating systems can achieve optimal performance, benefiting operators, consumers, and the environment alike.
We welcome the opportunity to collaborate on this groundbreaking initiative.